Quantum-Enhanced Optimizer Using Generative Models
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Solution Overview
Problem
Existing optimization techniques face challenges in translating real-world problems into polynomial unconstrained binary optimization (PUBO) expressions, leading to overhead and limitations in achieving computational advantage, especially for complex problems.
Innovation Solution
The development of quantum-enhanced optimizers (QEOs) using quantum generative models, such as Matrix Product States (MPS) and quantum-assisted generative adversarial networks, to generate new solution candidates with lower objective function values, bypassing the need for translation and enhancing the performance of classical optimizers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If quantum generative models are used to generate new solution candidates, then the quality of minima found is improved, but the complexity of the optimization system increases
Solution Approach 1:
The patent introduces a quantum generative model as an intermediary component between the cost function evaluator and the solution selection process. This quantum model learns the underlying distribution of high-quality solutions and generates new candidate solutions that classical optimizers might miss, thereby improving minima quality while maintaining a modular system architecture that manages complexity through clear separation of concerns.
Solution Approach 2:
The optimization system is segmented into distinct functional modules: a cost function evaluator, a quantum generative model trainer, and a solution sampler. This segmentation allows each component to be optimized independently and enables the system to leverage both classical and quantum computational resources without requiring complete system redesign, thus managing complexity while improving performance.
2Productivity
If quantum generative models are used to generate new solution candidates, then the number of cost function evaluations is reduced, but the overhead of quantum model training increases
Solution Approach 1:
The quantum generative model is trained in advance on a dataset of high-quality solutions obtained from classical optimizers or random sampling. This preliminary training phase allows the model to learn the distribution of optimal solutions before the actual optimization task begins. During the optimization phase, the pre-trained model can rapidly generate new candidate solutions without requiring additional costly cost function evaluations, thus reducing overall productivity loss despite the initial training time investment.
3Device complexity
If quantum generative models bypass PUBO translation, then the overhead of variable translation is eliminated, but the adaptability to arbitrary objective functions must be maintained
Solution Approach 1:
The quantum generative model is designed as a universal framework that can handle arbitrary objective functions without requiring translation to PUBO form. The model learns the distribution of solutions directly from cost function evaluations, making it adaptable to any optimization problem type (continuous, discrete, combinatorial) while eliminating the need for problem-specific translation overhead. This universality is achieved through the model's ability to work with any cost function interface.
Data Source
AI summary
A system and method for a quantum-enhanced optimizer (QEO) using quantum generative models to achieve lower minimum cost functions than classical or other known optimizers. In a first embodiment, the QEO operates as a booster to enhance the performance of known stand-alone optimizers in complex instances where known optimizers have limitations. In a second embodiment, the QEO operates as a stand-alone optimizer for finding a minimum with the least number of cost-function evaluations. The disclosed QEO methods outperform known optimizers, including Bayesian optimizers. The disclosed quantum-enhanced optimization methods may be based on tensor networks. The generative models may also be based on classical, quantum, or hybrid quantum-classical approaches, including Quantum Circuit Associative Adversarial Networks (QC-AAN) and Quantum Circuit Born Machines (QCBM). In another embodiment, an evolutionary generative algorithm (EGA) uses a generative model and a traditional optimizer within an evolutionary algorithmic framework to generate improved solutions.